CryoYeTi · Building Invariant Representations: Computational Innovations for Cryo-ET Data Analysis

Cryo-Electron Tomography (Cryo-ET) is a powerful technique that allows observing the 3D structure of biological molecules in their native cellular environment, and as such, has a unique promise to transform our understanding of biology at the mesoscopic level. Currently, its full impact is constrained by the difficulties associated with interpreting the 3D tomograms in terms of the molecular reality they represent (the so-called assignment problem), which mainly happens due to low resolution and noise inherent to the method.Our research goal is to develop innovative and highly efficient computational tools that will help to overcome the existing challenges and help to address the assignment problem. We shall achieve this goal by building invariant representations of 3D volumetric data that will allow us to significantly speed up the processing of tomograms and expand the scale and accuracy of the analysis that can be performed. This mathematical foundation will allow us to strengthen both template-based and template-free approaches to the assignment problem, addressing the critical weaknesses of the current state of the art, specifically, the high computational costs of the former and dependence on scarce training data of the latter. To demonstrate the whole capacity of our approach, we shall apply it to novel tomograms of highly important whole-cell biological samples obtained by our experimental collaborators.This highly multidisciplinary project (math+computation+biology) rests on the unique theoretical/computational competencies brought by the supervisor and the candidate, and involves a key experimental collaboration, bolstering the exchange of scientific knowledge. The planned training of the candidate in mathematical/algorithmic methods, the peculiarities of the Cryo-ET data, and key transversal skills will strengthen his profile as a specialist in integrative molecular modeling and ensure a successful transition to the next career stage.